This a model from the article: Role for G protein Gbetagamma isoform specificity in synaptic signal processing: a computational study. Bertram R, Arnot MI, Zamponi GW. J Neurophysiol 2002 May;87(5):2612-23 11976397 , Abstract: Computational modeling is used to investigate the functional impact of G protein-mediated presynaptic autoinhibition on synaptic filtering properties. It is demonstrated that this form of autoinhibition, which is relieved by depolarization, acts as a high-pass filter. This contrasts with vesicle depletion, which acts as a low-pass filter. Model parameters are adjusted to reproduce kinetic slowing data from different Gbetagamma dimeric isoforms, which produce different degrees of slowing. With these sets of parameter values, we demonstrate that the range of frequencies filtered out by the autoinhibition varies greatly depending on the Gbetagamma isoform activated by the autoreceptors. It is shown that G protein autoinhibition can enhance the spatial contrast between a spatially distributed high-frequency signal and surrounding low-frequency noise, providing an alternate mechanism to lateral inhibition. It is also shown that autoinhibition can increase the fidelity of coincidence detection by increasing the signal-to-noise ratio in the postsynaptic cell. The filter cut, the input frequency below which signals are filtered, depends on several biophysical parameters in addition to those related to Gbetagamma binding and unbinding. By varying one such parameter, the rate at which transmitter unbinds from autoreceptors, we show that the filter cut can be adjusted up or down for several of the Gbetagamma isoforms. This allows for great synapse-to-synapse variability in the distinction between signal and noise. This model was taken from the CellML repository and automatically converted to SBML. The original model was: Bertram R, Arnot MI, Zamponi GW. (2002) - version=1.0 The original CellML model was created by: Catherine Lloyd c.lloyd@auckland.ac.nz The University of Auckland This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.